It would seem that no one's immune from the effects imposed by our increasingly sophisticated artificial intelligence and robotics — not even doctors. As research from Indiana University has revealed, a new computer program is doing a better job than doctors when it comes to both diagnosing and treating health conditions — and by a significant margin.

The system, which uses decision making processes similar to the Jeopardy-bot, Watson, was recently given the task of analyzing and predicting the health outcomes of 500 real individuals. After plugging in the relevant data — which mostly had to do with clinical depression and chronic diseases like high blood pressure and diabetes — researchers Kris Hauser and Casey Bennett compared the outcomes to the simulated treatment prescriptions.

Here's what they discovered: The computer was nearly 42% better at diagnosing illnesses and prescribing effective treatments than human doctors.

To achieve such an impressive outcome, the computer used an artificial intelligence framework that combines Markov Decision Processes and Dynamic Decision Networks. It's a framework that employs sequential decision-making, allowing the computer to simulate a series of alternative treatment paths into the future. The machine can also make assumptions about a patient's health when data is not available, and re-adjust when new data is introduced. The system is also non-disease-specific and adaptable to virtually any health issue.

Essentially, it can deliberate about the future and consider all the different possible sequences of actions and effects in advance — even when it's unsure of the effects.

The researchers also noticed a significant disparity in cost. Doctors charge about $497 per unit of outcome change, whereas the computer cost only $189 for the same measure.

Looking ahead to the future, however, the researchers remain cautious about completely handing over the reigns to computers.

"Even with the development of new AI techniques that can approximate or even surpass human decision-making performance, we believe that the most effective long-term path could be combining artificial intelligence with human clinicians," they said in a statement. "Let humans do what they do well, and let machines do what they do well. In the end, we may maximize the potential of both."